Demand-based Sensor Data Gathering with Multi-Query Optimization

Summary: Demand-driven sensor acquisition jointly optimizes read and transmission schedules across concurrent queries, reducing bandwidth, energy, and device work while preserving accuracy. Combines multi-query sharing with ML-based adaptive sampling and validates it on deployed hardware and real traces. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: hdc210fcfc9ff461d
Venue: VLDB
Year: 2020
Pagerank: 4.9793485e-05
Overall Rank: 12,100 | 18.65%
DOI: 10.14778/3415478.3415479

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Julius Hülsmann (Technical University of Berlin)
2. Jonas Traub (Technical University of Berlin)
3. Volker Markl (Technical University of Berlin)

BibTeX Citation

@article{hulsmann_vldb20,
        title = {{Demand-based Sensor Data Gathering with Multi-Query Optimization}},
        author = {Hülsmann, Julius and Traub, Jonas and Markl, Volker},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {12},
        pages = {2801--2804},
        doi = {10.14778/3415478.3415479},
        url = {https://doi.org/10.14778/3415478.3415479},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
231	Storm @Twitter	2014	SIGMOD	0.00023841089
5,343	The NebulaStream Platform: Data and Application Management for the Internet of Things	2020	CIDR	6.1708665e-05

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